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All Journal JOIV : International Journal on Informatics Visualization JOURNAL OF SCIENCE AND SOCIAL RESEARCH IT (INFORMATIC TECHNIQUE) JOURNAL Majalah Ilmiah UPI YPTK INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komtekinfo Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) PUBLIDIMAS CSRID LEX JUSTITIA
Negoro, Wahyu Saptha
Universitas Potensi Utama
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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JARINGAN SYARAF TIRUAN DENGAN LEARNING VECTOR QUANTIZATION (LVQ) UNTUK KLASIFIKASI DAUN: ARTIFICIAL NEURAL NETWORKS USING LEARNING VECTOR QUANTIZATION (LVQ) FOR LEAF CLASSIFICATION Soeheri; Rita Sari; Wahyu Saptha Negoro; Yuhandri
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

Leaves are one part of a plant species that is commonly used to classify plant and plant species. The process of assisting various types of leaves usually involves experts using a herbarium, which is a collection of preserved plant specimens. Leaf classification is the detection of different types of leaves, where there are 2 types of leaves including Magnolia Soulangeana and Invillea leaves. The training data contains 30 images consisting of 15 each of the 2 types of leaves, then the test data contains 20 images which are also taken from the 2 types of leaves. So that the total images used are 50 leaf images. The leaf classification uses feature extraction and the method used in the classifier is Learning Vector Quantization (LVQ) which is a pattern classification method in which each output unit represents a particular class or group. The test results showed that the process of calling Magnolia Soulangeana and Bougainvillea leaves in this experiment was successful with 80% detection Keywords—Leaf classification, Learning Vector Quantization, Artificial Neural Networks, Feature extraction.
Segmentation and Classification of Vitamin C Content in Red Chili Pepper Images Using the Linear Discriminant Analysis (LDA) Method: Segmentation and Classification of Vitamin C Content in Red Chili Pepper Images Using the Linear Discriminant Analysis (LDA) Method Ramadhanu, Agung; Chan, Fajri Rinaldi; Yasmin, Nabilla; Negoro, Wahyu Saptha; Mardison, Mardison; Hendri, Halifia
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.149-162

Abstract

The vitamin C content in red chili peppers plays a crucial role in meeting nutritional needs, particularly in free nutritious lunch programs. Red chili peppers are one of the essential sources of vitamin C in daily consumption. However, vitamin C content in chilies can degrade due to storage and drying processes. This study develops a segmentation and classification method for vitamin C content in red chili pepper images using Linear Discriminant Analysis (LDA) as a faster and more efficient alternative to conventional laboratory methods. The dataset consists of 100 red chili images categorized into fresh and dried chilies. The analysis process includes preprocessing, feature extraction of color and texture (RGB, HSV, GLCM), dimensionality reduction, and classification using LDA. Experimental results show that this method achieves 99% accuracy on training data and 97% on test data, demonstrating that digital image processing can serve as a non-destructive approach for food quality estimation. This approach has the potential to be applied in food quality monitoring within the food industry and public nutrition programs.
JARINGAN SYARAF TIRUAN DENGAN LEARNING VECTOR QUANTIZATION (LVQ) UNTUK KLASIFIKASI DAUN: ARTIFICIAL NEURAL NETWORKS USING LEARNING VECTOR QUANTIZATION (LVQ) FOR LEAF CLASSIFICATION Soeheri; Sari, Rita; Wahyu Saptha Negoro; Yuhandri
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Leaves are one part of a plant species that is commonly used to classify plant and plant species. The process of assisting various types of leaves usually involves experts using a herbarium, which is a collection of preserved plant specimens. Leaf classification is the detection of different types of leaves, where there are 2 types of leaves including Magnolia Soulangeana and Invillea leaves. The training data contains 30 images consisting of 15 each of the 2 types of leaves, then the test data contains 20 images which are also taken from the 2 types of leaves. So that the total images used are 50 leaf images. The leaf classification uses feature extraction and the method used in the classifier is Learning Vector Quantization (LVQ) which is a pattern classification method in which each output unit represents a particular class or group. The test results showed that the process of calling Magnolia Soulangeana and Bougainvillea leaves in this experiment was successful with 80% detection Keywords—Leaf classification, Learning Vector Quantization, Artificial Neural Networks, Feature extraction.
Automated Fruit Image Classification Based on HSV Features, Morphological Segmentation, and Extreme Learning Machine Agung Ramadhanu; Halifia Hendri; Wahyu Saptha Negoro; Mardison Mardison; Larissa Navia Rani; Sofika Enggari; Muhammad Reza Putra
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.135-147

Abstract

Fruit image classification plays a crucial role in smart agriculture, particularly in automating sorting and quality control processes. This study proposes a fruit classification system by integrating HSV color space conversion, adaptive thresholding, morphological segmentation, and the Extreme Learning Machine (ELM) algorithm. The dataset consists of three fruit classes—apple, pineapple, and watermelon—with a total of 480 images, divided into 360 training samples and 120 testing samples. Image preprocessing involves resizing, HSV conversion, noise reduction through morphological operations, and feature extraction based on color and shape characteristics. The extracted features are used to train and test an ELM model. To improve classification performance and address potential overfitting in traditional ELM, this study introduces a new development called the Extended Extreme Learning Machine (EELM). The key innovation lies in modifying the calculation of the output weights βj, where a regularization term is introduced using ridge regression to stabilize learning and improve generalization. Experimental results show that the proposed system achieves 100% accuracy on the training data and an average accuracy of 83.3% on the testing data. The system also demonstrates robustness in handling varying lighting conditions and fruit shapes. These improvements enable EELM to better handle noisy or complex data by preventing over-reliance on randomly initialized hidden layer parameters. Consequently, EELM demonstrates improved reliability, making it more suitable for deployment in resourceconstrained real-world environments such as mobile or embedded systems.
Co-Authors Achmad Syahrian Adinda Destari, Ratih Agung Ramadhanu Andreas Jorghy Parapat Andri Armaginda Siregar Annisa Ashari Arbana Syamanta Asbon Hendra Azhar Ashari, Annisa Azhar, Asbon Hendra Bill Raj Boby Daniel Simatupang Bualazatulo Laia Chan, Fajri Rinaldi Diki Ramanda Diyo Mollana Fazri Dwi Robiul Rochmawati Edy Victor Haryanto Fhery Agustin Fujiati Fujiati Halifia Hendri Hendra Azhar, Asbon Heru Fredi Idzhari Rahman Ivo Ramadhani Jalaluddin Nasution Johan Larissa Navia Rani, Larissa Lima Hartimar Rambe Linda Wahyuni Manza, Yuke Mardison Mardison Mardison Mardison Mhd. Agung Irnanda Muhammad Al Adib Muhammad Reza Putra Nanda Setiawan Rahmad Santoso Rahmat Humala Putra Hasibuan Ramadhani, Ivo Rambe, Lima Hartimar Ratih Adinda Destari Ratih Adinda Destari, Ratih Adinda Rini Oktari Batubara Rita Sari Rita Sari Romi Antoni Saiful Bukhori Simatupang, Boby Daniel Soeheri Soeheri Sofika Enggari Sumijan Sumijan Susiana Khosasih Syahrian, Achmad Yasmin, Nabilla Yiska Dayanti Zagoto Yuhandri Yuhandri, Yuhandri Yuke Manza